Data Viz in PlottingĀ¶

InĀ [Ā ]:
import pandas as pd  # For data manipulation and analysis
import numpy as np  # For numerical computations
import matplotlib.pyplot as plt  # For creating static, animated, and interactive visualizations
import seaborn as sns  # For statistical data visualization based on Matplotlib
import scipy  # For scientific and technical computing (including optimization, integration, and statistics)
InĀ [4]:
import pandas as pd 
import numpy as np
# Creating realistic data for employees
data = {
    'Employee ID': np.arange(1001, 1011),
    'Employee Name': ['Satender Kumar', 'data 1', 'Jane Smith', 'Robert Brown', 'Emily Davis', 'Michael Wilson', 'Sarah Taylor', 'David Lee', 'Laura Johnson', 'James White'],
    'Department': ['Data Analyst', 'IT', 'Finance', 'Marketing', 'Sales', 'Operations', 'R&D', 'Support', 'Admin', 'Legal'],
    'Age': [24, np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60)],
    'Location': ['London, Canada', 'Toronto', 'London', 'Sydney', 'San Francisco', 'Paris', 'Berlin', 'Tokyo', 'Dubai', 'Singapore'],
    'Salary': np.random.randint(50000, 150000, size=10),
    'Years with Company': np.random.randint(1, 15, size=10),
    'Position': ['Data Analyst', 'Developer', 'Analyst', 'Designer', 'Consultant', 'Engineer', 'Scientist', 'Support Agent', 'Admin Assistant', 'Lawyer'],
    'Performance Score': np.random.randint(1, 5, size=10),
    'Bonus': np.random.randint(1000, 10000, size=10),
    'Gender': ['Male', 'Male', 'Female', 'Male', 'Female', 'Male', 'Female', 'Male', 'Female', 'Male'],
    'Marital Status': ['Single', 'Single', 'Married', 'Single', 'Single', 'Married', 'Married', 'Single', 'Married', 'Single'],
    'Education': ['Bachelor', 'Master', 'PhD', 'Bachelor', 'Master', 'PhD', 'Bachelor', 'Master', 'Bachelor', 'Master'],
    'Hire Date': pd.to_datetime(['2019-06-12', '2015-07-23', '2012-09-05', '2018-11-30', '2013-05-19', '2019-02-14', '2020-08-21', '2016-06-03', '2014-01-28', '2017-03-15']),
    'Overtime Hours': np.random.randint(0, 20, size=10),
    'Sick Days Taken': np.random.randint(0, 10, size=10),
    'Vacation Days Taken': np.random.randint(5, 20, size=10),
    'Training Hours': np.random.randint(10, 50, size=10),
    'Certifications': ['Yes', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'No', 'Yes'],
    'Supervisor': ['Anna Smith', 'Brian Adams', 'Clara Jones', 'Daniel Martin', 'Eva Rodriguez', 'Frank Bell', 'Grace Moore', 'Hannah Lewis', 'Ivan Scott', 'Jake Miller']
}

# Creating the DataFrame
df = pd.DataFrame(data)
InĀ [10]:
df
Out[10]:
Employee ID Employee Name Department Age Location Salary Years with Company Position Performance Score Bonus Gender Marital Status Education Hire Date Overtime Hours Sick Days Taken Vacation Days Taken Training Hours Certifications Supervisor
0 1001 Satender Kumar Data Analyst 24 London, Canada 50379 3 Data Analyst 2 6598 Male Single Bachelor 2019-06-12 19 3 11 48 Yes Anna Smith
1 1002 data 1 IT 37 Toronto 98030 3 Developer 4 6601 Male Single Master 2015-07-23 3 5 14 29 No Brian Adams
2 1003 Jane Smith Finance 42 London 54742 13 Analyst 3 1951 Female Married PhD 2012-09-05 18 7 12 19 Yes Clara Jones
3 1004 Robert Brown Marketing 50 Sydney 79468 7 Designer 4 3987 Male Single Bachelor 2018-11-30 16 6 6 41 No Daniel Martin
4 1005 Emily Davis Sales 39 San Francisco 140576 5 Consultant 1 6045 Female Single Master 2013-05-19 15 7 15 14 Yes Eva Rodriguez
5 1006 Michael Wilson Operations 55 Paris 149810 2 Engineer 2 2484 Male Married PhD 2019-02-14 18 1 13 22 No Frank Bell
6 1007 Sarah Taylor R&D 46 Berlin 122118 1 Scientist 2 1421 Female Married Bachelor 2020-08-21 17 7 17 11 Yes Grace Moore
7 1008 David Lee Support 56 Tokyo 121107 11 Support Agent 4 3473 Male Single Master 2016-06-03 14 7 9 33 Yes Hannah Lewis
8 1009 Laura Johnson Admin 38 Dubai 143323 8 Admin Assistant 1 5515 Female Married Bachelor 2014-01-28 2 8 17 12 No Ivan Scott
9 1010 James White Legal 48 Singapore 51334 12 Lawyer 1 3612 Male Single Master 2017-03-15 9 5 6 20 Yes Jake Miller
InĀ [11]:
import pandas as pd 
import numpy as np
InĀ [13]:
# Creating realistic data for a second set of employees
data1 = {
    'Employee ID': np.arange(1011, 1021),
    'Employee Name': ['Satender Kumar', 'data 1', 'Chris Evans', 'Natalie Portman', 'Tom Holland', 'Emma Watson', 'Daniel Radcliffe', 'Scarlett Johansson', 'Robert Downey Jr.', 'Mark Ruffalo'],
    'Department': ['Data Analyst', 'HR', 'IT', 'Marketing', 'Finance', 'Sales', 'R&D', 'Operations', 'Legal', 'Support'],
    'Age': [24, np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60), np.random.randint(25, 60)],
    'Location': ['London, Canada', 'Los Angeles', 'New York', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio', 'San Diego', 'Dallas'],
    'Salary': np.random.randint(60000, 160000, size=10),
    'Years with Company': np.random.randint(1, 20, size=10),
    'Position': ['Data Analyst', 'HR Manager', 'IT Specialist', 'Marketing Coordinator', 'Financial Analyst', 'Sales Manager', 'Research Scientist', 'Operations Manager', 'Legal Advisor', 'Support Specialist'],
    'Performance Score': np.random.randint(1, 5, size=10),
    'Bonus': np.random.randint(2000, 12000, size=10),
    'Gender': ['Male', 'Male', 'Female', 'Female', 'Male', 'Female', 'Male', 'Female', 'Male', 'Male'],
    'Marital Status': ['Single', 'Married', 'Single', 'Single', 'Married', 'Single', 'Single', 'Married', 'Single', 'Married'],
    'Education': ['Master', 'Bachelor', 'Master', 'PhD', 'Bachelor', 'Master', 'PhD', 'Bachelor', 'Master', 'PhD'],
    'Hire Date': pd.to_datetime(['2018-07-15', '2014-03-22', '2011-10-12', '2017-04-17', '2015-09-23', '2016-11-01', '2019-05-11', '2020-07-08', '2013-08-19', '2012-01-09']),
    'Overtime Hours': np.random.randint(0, 25, size=10),
    'Sick Days Taken': np.random.randint(0, 8, size=10),
    'Vacation Days Taken': np.random.randint(7, 22, size=10),
    'Training Hours': np.random.randint(15, 55, size=10),
    'Certifications': ['Yes', 'Yes', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'No'],
    'Supervisor': ['John Smith', 'Michael Johnson', 'Patricia Williams', 'Linda Brown', 'Barbara Jones', 'Elizabeth Garcia', 'Susan Martinez', 'Jessica Hernandez', 'Sarah Lopez', 'Karen Wilson']
}

# Creating the second DataFrame
df1 = pd.DataFrame(data1)
InĀ [15]:
df1
Out[15]:
Employee ID Employee Name Department Age Location Salary Years with Company Position Performance Score Bonus Gender Marital Status Education Hire Date Overtime Hours Sick Days Taken Vacation Days Taken Training Hours Certifications Supervisor
0 1011 Satender Kumar Data Analyst 24 London, Canada 95392 5 Data Analyst 2 3416 Male Single Master 2018-07-15 3 0 18 42 Yes John Smith
1 1012 data 1 HR 56 Los Angeles 75471 17 HR Manager 1 9690 Male Married Bachelor 2014-03-22 18 2 14 41 Yes Michael Johnson
2 1013 Chris Evans IT 30 New York 129083 12 IT Specialist 2 4132 Female Single Master 2011-10-12 16 7 16 39 No Patricia Williams
3 1014 Natalie Portman Marketing 27 Chicago 104039 3 Marketing Coordinator 3 10256 Female Single PhD 2017-04-17 9 6 11 36 Yes Linda Brown
4 1015 Tom Holland Finance 56 Houston 138405 5 Financial Analyst 3 10453 Male Married Bachelor 2015-09-23 16 1 17 45 No Barbara Jones
5 1016 Emma Watson Sales 35 Phoenix 145713 19 Sales Manager 3 11268 Female Single Master 2016-11-01 2 7 9 51 Yes Elizabeth Garcia
6 1017 Daniel Radcliffe R&D 41 Philadelphia 62592 1 Research Scientist 2 9166 Male Single PhD 2019-05-11 17 2 21 24 No Susan Martinez
7 1018 Scarlett Johansson Operations 44 San Antonio 119897 10 Operations Manager 3 8533 Female Married Bachelor 2020-07-08 21 2 13 37 Yes Jessica Hernandez
8 1019 Robert Downey Jr. Legal 27 San Diego 92816 3 Legal Advisor 4 9062 Male Single Master 2013-08-19 9 3 10 16 Yes Sarah Lopez
9 1020 Mark Ruffalo Support 46 Dallas 144765 1 Support Specialist 3 4142 Male Married PhD 2012-01-09 16 7 21 54 No Karen Wilson
InĀ [17]:
merged_df = pd.merge(df, df1, on='Employee ID', suffixes=('_df', '_df1'), how='outer')
print(merged_df)

    Employee ID Employee Name_df Department_df  Age_df     Location_df  \
0          1001   Satender Kumar  Data Analyst    24.0  London, Canada   
1          1002           data 1            IT    37.0         Toronto   
2          1003       Jane Smith       Finance    42.0          London   
3          1004     Robert Brown     Marketing    50.0          Sydney   
4          1005      Emily Davis         Sales    39.0   San Francisco   
5          1006   Michael Wilson    Operations    55.0           Paris   
6          1007     Sarah Taylor           R&D    46.0          Berlin   
7          1008        David Lee       Support    56.0           Tokyo   
8          1009    Laura Johnson         Admin    38.0           Dubai   
9          1010      James White         Legal    48.0       Singapore   
10         1011              NaN           NaN     NaN             NaN   
11         1012              NaN           NaN     NaN             NaN   
12         1013              NaN           NaN     NaN             NaN   
13         1014              NaN           NaN     NaN             NaN   
14         1015              NaN           NaN     NaN             NaN   
15         1016              NaN           NaN     NaN             NaN   
16         1017              NaN           NaN     NaN             NaN   
17         1018              NaN           NaN     NaN             NaN   
18         1019              NaN           NaN     NaN             NaN   
19         1020              NaN           NaN     NaN             NaN   

    Salary_df  Years with Company_df      Position_df  Performance Score_df  \
0     50379.0                    3.0     Data Analyst                   2.0   
1     98030.0                    3.0        Developer                   4.0   
2     54742.0                   13.0          Analyst                   3.0   
3     79468.0                    7.0         Designer                   4.0   
4    140576.0                    5.0       Consultant                   1.0   
5    149810.0                    2.0         Engineer                   2.0   
6    122118.0                    1.0        Scientist                   2.0   
7    121107.0                   11.0    Support Agent                   4.0   
8    143323.0                    8.0  Admin Assistant                   1.0   
9     51334.0                   12.0           Lawyer                   1.0   
10        NaN                    NaN              NaN                   NaN   
11        NaN                    NaN              NaN                   NaN   
12        NaN                    NaN              NaN                   NaN   
13        NaN                    NaN              NaN                   NaN   
14        NaN                    NaN              NaN                   NaN   
15        NaN                    NaN              NaN                   NaN   
16        NaN                    NaN              NaN                   NaN   
17        NaN                    NaN              NaN                   NaN   
18        NaN                    NaN              NaN                   NaN   
19        NaN                    NaN              NaN                   NaN   

    Bonus_df  ... Gender_df1 Marital Status_df1 Education_df1 Hire Date_df1  \
0     6598.0  ...        NaN                NaN           NaN           NaT   
1     6601.0  ...        NaN                NaN           NaN           NaT   
2     1951.0  ...        NaN                NaN           NaN           NaT   
3     3987.0  ...        NaN                NaN           NaN           NaT   
4     6045.0  ...        NaN                NaN           NaN           NaT   
5     2484.0  ...        NaN                NaN           NaN           NaT   
6     1421.0  ...        NaN                NaN           NaN           NaT   
7     3473.0  ...        NaN                NaN           NaN           NaT   
8     5515.0  ...        NaN                NaN           NaN           NaT   
9     3612.0  ...        NaN                NaN           NaN           NaT   
10       NaN  ...       Male             Single        Master    2018-07-15   
11       NaN  ...       Male            Married      Bachelor    2014-03-22   
12       NaN  ...     Female             Single        Master    2011-10-12   
13       NaN  ...     Female             Single           PhD    2017-04-17   
14       NaN  ...       Male            Married      Bachelor    2015-09-23   
15       NaN  ...     Female             Single        Master    2016-11-01   
16       NaN  ...       Male             Single           PhD    2019-05-11   
17       NaN  ...     Female            Married      Bachelor    2020-07-08   
18       NaN  ...       Male             Single        Master    2013-08-19   
19       NaN  ...       Male            Married           PhD    2012-01-09   

    Overtime Hours_df1  Sick Days Taken_df1  Vacation Days Taken_df1  \
0                  NaN                  NaN                      NaN   
1                  NaN                  NaN                      NaN   
2                  NaN                  NaN                      NaN   
3                  NaN                  NaN                      NaN   
4                  NaN                  NaN                      NaN   
5                  NaN                  NaN                      NaN   
6                  NaN                  NaN                      NaN   
7                  NaN                  NaN                      NaN   
8                  NaN                  NaN                      NaN   
9                  NaN                  NaN                      NaN   
10                 3.0                  0.0                     18.0   
11                18.0                  2.0                     14.0   
12                16.0                  7.0                     16.0   
13                 9.0                  6.0                     11.0   
14                16.0                  1.0                     17.0   
15                 2.0                  7.0                      9.0   
16                17.0                  2.0                     21.0   
17                21.0                  2.0                     13.0   
18                 9.0                  3.0                     10.0   
19                16.0                  7.0                     21.0   

    Training Hours_df1 Certifications_df1     Supervisor_df1  
0                  NaN                NaN                NaN  
1                  NaN                NaN                NaN  
2                  NaN                NaN                NaN  
3                  NaN                NaN                NaN  
4                  NaN                NaN                NaN  
5                  NaN                NaN                NaN  
6                  NaN                NaN                NaN  
7                  NaN                NaN                NaN  
8                  NaN                NaN                NaN  
9                  NaN                NaN                NaN  
10                42.0                Yes         John Smith  
11                41.0                Yes    Michael Johnson  
12                39.0                 No  Patricia Williams  
13                36.0                Yes        Linda Brown  
14                45.0                 No      Barbara Jones  
15                51.0                Yes   Elizabeth Garcia  
16                24.0                 No     Susan Martinez  
17                37.0                Yes  Jessica Hernandez  
18                16.0                Yes        Sarah Lopez  
19                54.0                 No       Karen Wilson  

[20 rows x 39 columns]
InĀ [19]:
df.plot()
Out[19]:
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InĀ [21]:
df1.plot()
Out[21]:
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InĀ [54]:
# Pie chart for department distribution in df1
df1['Department'].value_counts().plot(kind='pie', autopct='%1.1f%%', title='Department Distribution in df1')
plt.ylabel('')  # Remove the y-label for a cleaner look
plt.show()
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InĀ [56]:
# Stacked bar plot showing count of employees by Department and Gender in df
df.groupby(['Department', 'Gender']).size().unstack().plot(kind='bar', stacked=True, figsize=(10, 7))
plt.title('Stacked Bar Plot for Department and Gender in df')
plt.ylabel('Number of Employees')
plt.show()
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InĀ [57]:
# Area plot showing salary over years with company in df1
df1.plot.area(x='Years with Company', y='Salary', figsize=(10, 7), alpha=0.4)
plt.title('Area Plot for Salary Over Years with Company in df1')
plt.ylabel('Salary')
plt.show()
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InĀ [58]:
from mpl_toolkits.mplot3d import Axes3D

# 3D scatter plot in df
fig = plt.figure(figsize=(10, 7))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(df['Age'], df['Salary'], df['Performance Score'], c='r', marker='o')
ax.set_xlabel('Age')
ax.set_ylabel('Salary')
ax.set_zlabel('Performance Score')
plt.title('3D Scatter Plot in df')
plt.show()
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InĀ [59]:
# Hexbin plot for Age vs. Salary in df1
df1.plot.hexbin(x='Age', y='Salary', gridsize=20, cmap='Blues', figsize=(10, 7))
plt.title('Hexbin Plot for Age vs. Salary in df1')
plt.show()
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InĀ [61]:
# Hexbin plot for Age vs. Salary in df
df.plot.hexbin(x='Age', y='Salary', gridsize=20, cmap='Blues', figsize=(11, 8))
plt.title('Hexbin Plot for Age vs. Salary in df')
plt.show()
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InĀ [63]:
df.boxplot()
Out[63]:
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InĀ [30]:
df1.boxplot()
Out[30]:
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InĀ [32]:
import pandas as pd
import matplotlib.pyplot as plt

# Assuming df is your first DataFrame
df.hist(figsize=(12, 10), bins=15, grid=False)

# Display the plots for df
plt.suptitle('Histograms for df')
plt.show()
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InĀ [35]:
# Assuming df1 is your second DataFrame
df1.hist(figsize=(12, 10), bins=15, grid=False)

# Display the plots for df1
plt.suptitle('Histograms for df1')
plt.show()
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InĀ [36]:
# Box plots for df1
df1.boxplot(figsize=(12, 10))
plt.suptitle('Box Plots for df1')
plt.show()
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InĀ [37]:
# Box plots for df1
df.boxplot(figsize=(12, 10))
plt.suptitle('Box Plots for df')
plt.show()
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InĀ [44]:
# Scatter plot between 'Age' and 'Salary' in df1
df1.plot.scatter(x='Age', y='Salary', title='Age vs. Salary in df1')
plt.show()
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InĀ [45]:
# Scatter plot between 'Age' and 'Salary' in df
df.plot.scatter(x='Age', y='Salary', title='Age vs. Salary in df')
plt.show()
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InĀ [48]:
# Density plot for Age distribution in df1
df1['Age'].plot(kind='density', title='Density Plot for Age in df1')
plt.show()
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InĀ [51]:
import seaborn as sns

# Violin plot for Salary distribution in df
sns.violinplot(y='Salary', data=df)
plt.title('Salary Distribution in df')
plt.show()
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InĀ [52]:
# Pie chart for department distribution in df1
df1['Department'].value_counts().plot(kind='pie', autopct='%1.1f%%', title='Department Distribution in df1')
plt.ylabel('')  # Remove the y-label for a cleaner look
plt.show()
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InĀ [53]:
# Pie chart for department distribution in df
df['Department'].value_counts().plot(kind='pie', autopct='%1.1f%%', title='Department Distribution in df')
plt.ylabel('')  # Remove the y-label for a cleaner look
plt.show()
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InĀ [68]:
import numpy as np

# Radar chart for comparing different metrics for the first employee in df
categories = ['Age', 'Salary', 'Years with Company', 'Performance Score', 'Bonus']
values = df.loc[0, categories].values.flatten().tolist()

# Adding the first value to the end of the list to close the radar chart
values += values[:1]
angles = np.linspace(0, 2 * np.pi, len(categories), endpoint=False).tolist()
angles += angles[:1]

fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
ax.fill(angles, values, color='red', alpha=0.25)
ax.plot(angles, values, color='red', linewidth=2)
ax.set_yticklabels([])
ax.set_xticks(angles[:-1])
ax.set_xticklabels(categories)
plt.title('Radar Chart for First Employee in df')
plt.show()
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InĀ [69]:
import plotly.express as px

# Sunburst plot showing hierarchy of Department and Gender in df
fig = px.sunburst(df, path=['Department', 'Gender'], values='Salary')
fig.update_layout(title='Sunburst Plot for Department and Gender in df')
fig.show()
InĀ [72]:
# Joint plot for Age vs. Salary in df
sns.jointplot(x='Age', y='Salary', data=df, kind='reg', height=8)
plt.suptitle('Joint Plot for Age vs. Salary in df', y=1.03)
plt.show()
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InĀ [73]:
# Swarm plot for Performance Score across Department in df1
plt.figure(figsize=(10, 7))
sns.swarmplot(x='Department', y='Performance Score', data=df1)
plt.title('Swarm Plot for Performance Score Across Department in df1')
plt.xticks(rotation=90)
plt.show()
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